With the advent of both data availability and accelerated
GPU computations, deep neural network architectures (DNN)
have flourished over the past years and showed tremen-
dous success on several benchmarks for computer vision,
and audio and speech. However, DNN models demand high
training times for sensitive parameter tuning and network-
specific optimization like regularization techniques, which is
a rather engineering approach. Although DNNs are motivated
by neurobiological principles observed in the visual cortex, the
complete picture of the computations boils down to learning
filters detecting specific frequency characteristics like edges
in images or voice pitch in audio data. As the learning is
carried out in a supervised manner, the use of a huge amount
of labeled data has a crucial impact on high accuracy and other
evaluation measures.
The area of cognitive robotics is highly interdisciplinary
and advances in sensor and robot technology allow the in-
tegration of human-like capabilities. The aim of cognitive
skills in robots is motivated by their increased integration in
human's everyday life, whether as a companion in human-
robot interaction (HRI) or as an autonomous system in health
care and industry.
DNNs can only address part of what actually cognitive
systems need and which challenges they face. The focus
on supervised benchmarking shifts research on the multiple,
interconnected facets of cognitive robots to a rather engineered
approach accompanied by constraints on the data and the
environment. Critical investigations on DNNs also reveal that
due to their high dependence on apriori knowledge, they are
easily fooled [1]} and fail to cope with, e.g., real-world speech
data from children. Recent advances of (deep) unsupervised
networks closely related to neural information principles like
sparse autoencoders or self organizing maps may stimulate
again joining state-of-the-art algorithms successfully applied
to robot scenarios with deep architectures.
In the present paper, we will discuss the pros and cons
of DNNs in cognitive robotics. We highlight their usefulness
but also limitations, discussing also the potential of Reservoir
Computing in the context of cognitive robots.
